BiocBot - AI-Powered Study Assistant

BiocBot is an AI-powered study assistant platform that enables students to interact with course material in a chat-based format. Instructors can upload documents (PDFs, DOCX, or TXT), which are automatically parsed, chunked, and embedded into a vector database (Qdrant) for semantic search. When a student asks a question, the system retrieves relevant chunks and generates a response grounded in course content.

🚀 Features

• Document Management: Upload and organize course materials per lecture/unit
• Vector Search: Semantic search across documents using Qdrant
• AI Chat Interface: RAG-powered student chat with tutor and protege modes
• Per-Course Retrieval Mode: Instructor-controlled additive vs single-unit retrieval for chat
• Quiz Practice System: Self-paced AI-graded quizzes with attempt history
• Assessment Questions: Create and manage multiple-choice, true/false, and short-answer questions
• Flagging System: Students flag issues with questions; instructors review and respond
• Student Struggle Tracking: Activity logging to monitor and surface struggling students
• Course Structure: Organize content by units/lectures with publish controls
• User Management: Separate interfaces for instructors, TAs, and students
• TA Management: Instructors promote students to TAs with scoped permissions
• Onboarding Wizard: Guided AI-assisted course setup for instructors
• SAML / UBC CWL Auth: Shibboleth integration alongside local username/password auth
• User Agreement: Modal-gated terms acceptance before platform access
• Session Idle Timeout: Automatic logout after inactivity

🏗️ Architecture

BiocBot follows a split architecture with a public frontend and a private backend, adhering to clear separation of concerns for maintainability and security.

Tech Stack

• Frontend: HTML + Vanilla JS (no frameworks), styled via separate CSS files
• Backend: Node.js (Express 5), built with modular architecture
• Database: MongoDB (documents, user sessions, analytics, quiz attempts)
• Vector Database: Qdrant for semantic search and similarity retrieval
• Embeddings: UBC GenAI Toolkit with OpenAI (text-embedding-3-small)
• Authentication: Passport.js — local strategy + SAML / UBC Shibboleth

🛠️ Setup & Installation

Prerequisites

• Node.js v18.x or higher
• MongoDB instance
• Qdrant vector database (Docker recommended)
• OpenAI API key

1. Clone and Install

git clone <repository-url>
cd tlef-biocbot
npm install

2. Environment Configuration

Create a .env file in the root directory with the following variables:

# MongoDB Connection
MONGO_URI=mongodb://localhost:27017/biocbot

# Server Port
TLEF_BIOCBOT_PORT=8080

# Qdrant Configuration
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=super-secret-dev-key

# Embeddings Provider Configuration
EMBEDDING_PROVIDER=ubc-genai-toolkit-llm

# LLM Provider Settings
LLM_PROVIDER=openai
LLM_API_KEY=your-openai-api-key
OPENAI_MODEL=gpt-4.1-mini
LLM_EMBEDDING_MODEL=text-embedding-3-small

3. Start Services

Start Qdrant (Docker)

docker run -p 6333:6333 qdrant/qdrant

Start BiocBot

npm run dev

📚 Usage

For Instructors

1. Access: Navigate to /instructor
2. Onboarding: Complete the guided course setup wizard (AI-assisted topic extraction)
3. Upload Documents: Add course materials to units/lectures
4. Create Questions: Build multiple-choice, true/false, and short-answer assessments
5. Publish Units: Make content available to students
6. Quiz Settings: Enable quiz practice, select testable units, and control material access for failed answers
7. Retrieval Mode: On the course Home page, toggle "Use additive retrieval" to allow chat to include earlier published units in addition to the selected unit. When off, chat uses only the selected unit.
8. Manage TAs: Promote students to TAs via the TA Hub; assign course and flag permissions
9. Review Flags: View and respond to student-flagged question issues
10. Monitor Students: Use the Student Hub to review engagement and struggle activity

For Students

1. Access: Navigate to /student
2. Agreement: Accept the user agreement on first login
3. Course Selection: Choose your course
4. Chat Interface: Select a unit, then ask questions about course material
5. Quiz Practice: Practice assessment questions with immediate AI feedback and attempt history
6. Flag Questions: Report unclear or incorrect questions for instructor review
7. Chat History: Review past conversations

For TAs

1. Access: Navigate to /ta
2. Onboarding: Complete TA onboarding
3. Settings: Configure TA-specific options
4. Flagged Questions: Review and respond to flagged questions (if permitted)

🔍 Qdrant Integration

BiocBot uses Qdrant for vector-based semantic search:

• Automatic Document Processing: Documents are automatically chunked, embedded, and stored on upload
• Semantic Search: Find relevant content using natural language queries
• Course-Aware Search: Filter results by course and lecture
• Real-time Indexing: New documents are immediately searchable

API Endpoints

• GET /api/qdrant/status — Check Qdrant service status
• POST /api/qdrant/process-document — Process and store a document
• POST /api/qdrant/search — Semantic search across documents
• DELETE /api/qdrant/document/:id — Delete document chunks
• GET /api/qdrant/collection-stats — Get collection statistics

Visit /qdrant-test to test the Qdrant functionality interactively.

🔧 Development

Project Structure

tlef-biocbot/
├── public/                     # Frontend assets
│   ├── common/
│   │   └── scripts/            # Shared scripts (auth, login, idle-timer, etc.)
│   ├── instructor/             # Instructor interface
│   │   ├── scripts/            # home, settings, onboarding, ta-hub, student-hub, ...
│   │   └── *.html
│   ├── student/                # Student interface
│   │   ├── scripts/            # dashboard, quiz, history, flagged, ...
│   │   └── *.html
│   ├── ta/                     # TA interface
│   │   ├── scripts/
│   │   └── *.html
│   └── qdrant-test.html        # Qdrant testing page
├── src/                        # Backend source
│   ├── config/                 # Passport, app config
│   ├── middleware/             # Auth middleware (requireAuth, requireRole, etc.)
│   ├── models/                 # MongoDB models
│   ├── routes/                 # API route handlers
│   ├── services/               # Business logic (LLM, Qdrant, auth, tracker)
│   └── server.js               # Main server entry point
└── documents/                  # Project documentation

Key Models

Model	Collection	Purpose
Course	courses	Course metadata, lecture structure, quiz settings
User	users	Accounts, roles, preferences, struggle state
Document	documents	Uploaded files and parsed content
Question	embedded in Course	MC, TF, and short-answer questions per lecture
QuizAttempt	quizAttempts	Per-student quiz attempt records
FlaggedQuestion	flaggedQuestions	Student-reported question issues
StruggleActivity	struggleActivity	Student struggle state transitions
UserAgreement	useragreements	Terms acceptance records

Key Services

• LLMService (src/services/llm.js): AI chat responses and short-answer evaluation via UBC GenAI Toolkit
• QdrantService (src/services/qdrantService.js): Vector DB indexing and semantic search
• AuthService (src/services/authService.js): User registration, login, preferences
• TrackerService (src/services/tracker.js): Student engagement and struggle tracking
• prompts (src/services/prompts.js): System prompt management (base, tutor, protege, quizHelp modes)

Auth Middleware

• requireAuth — Must be logged in
• requireStudent / requireInstructor / requireInstructorOrTA — Role-based access
• requireStudentEnrolled — Must be enrolled in the requested course
• requireTAPermission(permission) — TA-scoped permission checks

📄 License

ISC License